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  • Open Access


    Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

    Mousa Al-Akhras1,2,*, Zainab Darwish2, Samer Atawneh1, Mohamed Habib1,3

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3719-3749, 2022, DOI:10.32604/cmc.2022.025196

    Abstract Association rules’ learning is a machine learning method used in finding underlying associations in large datasets. Whether intentionally or unintentionally present, noise in training instances causes overfitting while building the classifier and negatively impacts classification accuracy. This paper uses instance reduction techniques for the datasets before mining the association rules and building the classifier. Instance reduction techniques were originally developed to reduce memory requirements in instance-based learning. This paper utilizes them to remove noise from the dataset before training the association rules classifier. Extensive experiments were conducted to assess the accuracy of association rules with… More >

  • Open Access


    CARM: Context Based Association Rule Mining for Conventional Data

    Muhammad Shaheen1,*, Umair Abdullah2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3305-3322, 2021, DOI:10.32604/cmc.2021.016766

    Abstract This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative… More >

  • Open Access


    An Apriori-Based Learning Scheme towards Intelligent Mining of Association Rules for Geological Big Data

    Maojian Chen1,2,3, Xiong Luo1,2,3,*, Yueqin Zhu4, Yan Li1,2,3, Wenbing Zhao5, Jinsong Wu6

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 973-987, 2020, DOI:10.32604/iasc.2020.010129

    Abstract The past decade has witnessed the rapid advancements of geological data analysis techniques, which facilitates the development of modern agricultural systems. However, there remains some technical challenges that should be addressed to fully exploit the potential of those geological big data, while gathering massive amounts of data in this application field. Generally, a good representation of correlation in the geological big data is critical to making full use of multi-source geological data, while discovering the relationship in data and mining mineral prediction information. Then, in this article, a scheme is proposed towards intelligent mining of More >

  • Open Access


    Design of the Sports Training Decision Support System Based on the Improved Association Rule, the Apriori Algorithm

    Xinbao Wang*, Dawu Huang, Xuemin Zhao

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 755-763, 2020, DOI:10.32604/iasc.2020.010110

    Abstract In order to improve the judgment decision ability of the sports training effect, a design method of the sports training decision support system based on the improved association rule, the Apriori algorithm is proposed, and a phase space model of the sports training decision support data association rule distribution is constructed. The association rule mining method is used to support the data mining model of sports training, and the decision judgment of the sports training effect is carried out in the mixed cloud computing environment. The fuzzy information fusion and the data structure feature reorganization… More >

  • Open Access


    Personalised Product Recommendation Model Based on User Interest

    Jitao Zhang

    Computer Systems Science and Engineering, Vol.34, No.4, pp. 231-236, 2019, DOI:10.32604/csse.2019.34.231

    Abstract The scale of e-commerce systems is increasing and more and more products are being offered online. However, users must find their own desired products among a large amount of unrelated information, which makes it increasingly difficult for them to make a purchase. In order to solve this problem of information overload, and effectively assist e-commerce users to shop easily and conveniently, an e-commerce personalized recommendation system technology has been proposed. This paper introduces the design and implementation of a personalized product recommendation model based on user interest. The “shopping basket analysis” functional model centered on… More >

  • Open Access


    Weighted or Non-Weighted Negative Tree Pattern Discovery from SensorRich Environments

    Juryon Paik*

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 193-204, 2020, DOI:10.31209/2019.100000140

    Abstract It seems to be sure that the IoT is one of promising potential topics today. Sensors are the one that lead the current IoT revolution. The advances of sensor-rich environments produce the massive volume of raw data that is enlarging faster than the rate at which it is being handled. JSON is a lightweight data-interchange format and preferred for IoT applications. Before JSON, XML was de factor standard format for interchanging data. The common point is that their structure scheme is the tree. Tree structure provides data exchangeability and heterogeneity, which encourages user-flexibilities. Therefore, JSON More >

  • Open Access


    An Improved Algorithm for Mining Correlation Item Pairs

    Tao Li1, Yongzhen Ren1, *, Yongjun Ren2, Jinyue Xia3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 337-354, 2020, DOI:10.32604/cmc.2020.06462

    Abstract Apriori algorithm is often used in traditional association rules mining, searching for the mode of higher frequency. Then the correlation rules are obtained by detected the correlation of the item sets, but this tends to ignore low-support high-correlation of association rules. In view of the above problems, some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm. It can dig item sets with low-support but high-correlation. Although the algorithm has pruned the search space, it is not obvious that the performance of the running time… More >

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